HiC-Hiker

HiC-Hiker infers contig orientations within chromosome-length scaffolds from Hi-C contact data using probabilistic modeling to improve genome assembly accuracy.

Key Features:

• Probabilistic Modeling: Models Hi-C contact frequencies across contigs using a probabilistic framework to infer contig orientations.
• Viterbi Algorithm: Employs the Viterbi algorithm to determine the most probable orientation sequence of contigs, reducing errors for shorter contigs with fewer neighboring contacts.
• Error Reduction: Demonstrates reduced contig orientation error rates compared to 3D-DNA, reported from 4.3% down to 1.7%.
• Long-Range Information Utilization: Incorporates long-range Hi-C contacts between distal contigs to refine contact probability estimates and improve contig ordering.

Scientific Applications:

• De novo Genome Assembly: Orients contigs to produce chromosome-length scaffolds in de novo assemblies, including non-model species lacking established genome markers.
• Genomic Distance Estimation: Uses Hi-C contact frequencies to estimate genomic distances between loci for scaffold construction.
• Gene Prediction: Supports more accurate gene prediction by improving scaffold orientation.
• Read Alignment: Improves read alignment accuracy by providing more accurate scaffold structures.
• Synteny Block Estimation: Aids comparative genomics through more reliable estimation of synteny blocks.

Methodology:

Models Hi-C contact frequencies across contigs using a probabilistic framework and infers the most probable contig orientations with the Viterbi algorithm, optionally incorporating long-range Hi-C contacts to refine contact probability estimates.